python图像处理裁剪,OpenCV图像处理以在Python中裁剪图像的倾斜部分
I am trying to crop a portion of an image as shown below using opencv / PIL . I want to crop the rectangle area as shown in red lines in the image in the below link. It is tilted at an angle.
I used numpy slicing logic as below. But it doesn't crop at an angle. It crops a normal straight rectangle
rect = cv2.boundingRect(pts)
x,y,w,h = rect
cropped = img[y:y+h, x:x+w]
Also tried rotating the entire image at an angle and then cropping that part but it shrinks the resulting image
I am able to draw a rectangle on that image using the below code :
def draw_angled_rec(x0, y0, width, height, angle, img):
_angle = angle * math.pi / 180.0
b = math.cos(_angle) * 0.5
a = math.sin(_angle) * 0.5
pt0 = (int(x0 - a * height - b * width),
int(y0 + b * height - a * width))
pt1 = (int(x0 + a * height - b * width),
int(y0 - b * height - a * width))
pt2 = (int(2 * x0 - pt0[0]), int(2 * y0 - pt0[1]))
pt3 = (int(2 * x0 - pt1[0]), int(2 * y0 - pt1[1]))
cv2.line(img, pt0, pt1, (255,0,0), 3)
cv2.line(img, pt1, pt2, (255,0,0), 3)
cv2.line(img, pt2, pt3, (255,0,0), 3)
cv2.line(img, pt3, pt0, (255,0,0), 3)
Please suggest / advice a way to achieve it.
Thanks
解决方案
Here's a image extraction widget that allows you to rotate the image and select a ROI by clicking and dragging the mouse. The idea is to use the mouse to select the bounding box window where we can use Numpy slicing to crop the image. Since OpenCV does not let you draw an angled rectangle, you can bypass that by first rotating the image.
Once you have selected the ROI, you can then crop the image using the bounding box coordinates. If we consider (0,0) as the top left corner of the image with left-to-right as the x-direction and top-to-bottom as the y-direction and we have (x1, y1) as the top-left vertex and (x2,y2) as the bottom-right vertex of a ROI, we can crop the image by:
ROI = image[y1:y2, x1:x2]
We are able to do this since images are stored as a Numpy array in OpenCV. Here is a great resource for Numpy array indexing and slicing.
To use the widget:
left mouse click + drag - select ROI
right mouse click - reset image
r - rotate image clockwise 5 degrees
e - rotate image counter-clockwise 5 degrees
c - crop selected ROI
q - quit program
import cv2
import numpy as np
class ExtractImageWidget(object):
def __init__(self):
self.original_image = cv2.imread('plane.PNG')
# Resize image, remove if you want raw image size
self.original_image = cv2.resize(self.original_image, (640, 556))
self.clone = self.original_image.copy()
cv2.namedWindow('image')
cv2.setMouseCallback('image', self.extract_coordinates)
# Bounding box reference points and boolean if we are extracting coordinates
self.image_coordinates = []
self.angle = 0
self.extract = False
self.selected_ROI = False
def extract_coordinates(self, event, x, y, flags, parameters):
# Record starting (x,y) coordinates on left mouse button click
if event == cv2.EVENT_LBUTTONDOWN:
self.image_coordinates = [(x,y)]
self.extract = True
# Record ending (x,y) coordintes on left mouse bottom release
elif event == cv2.EVENT_LBUTTONUP:
self.image_coordinates.append((x,y))
self.extract = False
self.selected_ROI = True
self.crop_ROI()
# Draw rectangle around ROI
cv2.rectangle(self.clone, self.image_coordinates[0], self.image_coordinates[1], (0,255,0), 2)
cv2.imshow("image", self.clone)
# Clear drawing boxes on right mouse button click and reset angle
elif event == cv2.EVENT_RBUTTONDOWN:
self.clone = self.original_image.copy()
self.angle = 0
self.selected_ROI = False
def show_image(self):
return self.clone
def rotate_image(self, angle):
# Grab the dimensions of the image and then determine the center
(h, w) = self.original_image.shape[:2]
(cX, cY) = (w / 2, h / 2)
self.angle += angle
# grab the rotation matrix (applying the negative of the
# angle to rotate clockwise), then grab the sine and cosine
# (i.e., the rotation components of the matrix)
M = cv2.getRotationMatrix2D((cX, cY), -self.angle, 1.0)
cos = np.abs(M[0, 0])
sin = np.abs(M[0, 1])
# Compute the new bounding dimensions of the image
nW = int((h * sin) + (w * cos))
nH = int((h * cos) + (w * sin))
# Adjust the rotation matrix to take into account translation
M[0, 2] += (nW / 2) - cX
M[1, 2] += (nH / 2) - cY
# Perform the actual rotation and return the image
self.clone = cv2.warpAffine(self.original_image, M, (nW, nH))
self.selected_ROI = False
def crop_ROI(self):
if self.selected_ROI:
self.cropped_image = self.clone.copy()
x1 = self.image_coordinates[0][0]
y1 = self.image_coordinates[0][1]
x2 = self.image_coordinates[1][0]
y2 = self.image_coordinates[1][1]
self.cropped_image = self.cropped_image[y1:y2, x1:x2]
print('Cropped image: {} {}'.format(self.image_coordinates[0], self.image_coordinates[1]))
else:
print('Select ROI to crop before cropping')
def show_cropped_ROI(self):
cv2.imshow('cropped image', self.cropped_image)
if __name__ == '__main__':
extract_image_widget = ExtractImageWidget()
while True:
cv2.imshow('image', extract_image_widget.show_image())
key = cv2.waitKey(1)
# Rotate clockwise 5 degrees
if key == ord('r'):
extract_image_widget.rotate_image(5)
# Rotate counter clockwise 5 degrees
if key == ord('e'):
extract_image_widget.rotate_image(-5)
# Close program with keyboard 'q'
if key == ord('q'):
cv2.destroyAllWindows()
exit(1)
# Crop image
if key == ord('c'):
extract_image_widget.show_cropped_ROI()